取样门管的特性对微波取样器性能影响的分析

本文在概述微波取样器原理的基础上,重点研究了取样门管的特性对取样器性能的影响。并通过计算机辅助分析,为取样门管参数的选取提供了理论依据。     

The hierarchical Tobit model: A case study in Bayesian computing

In this paper we discuss the potentials of a new Bayesian inference tool, called the Gibbs sampler, for the analysis of the censored regression or Tobit model. Tobit models have a wide range of applications in empirical sciences, like econometrics and biometrics. The estimation results of the simple Tobit model will be compared to a hierarchical Tobit model, and the Gibbs sampling approach to the related classical algorithm of expectation-maximisation (EM). The underlying botanical example of this paper is concerned with the censoring mechanism in plant reproduction and proposes the Bayesian Tobit model for the growth relationship between the reproductive part and the rest of the plant.     

MIMO系统中低复杂度的MCMC迭代检测算法

针对多输入多输出(MIMO)系统中现行的马尔科夫链蒙特卡洛(MCMC)检测算法复杂度较高的问题,提出了一种SIC-MMSE算法辅助的MCMC检测算法,信号的预估计值和软信息均作为软输入软输出(SISO)检测器的输入,提高了吉布斯采样值的可信度,进而减少采样点数。仿真结果表明,同样的信噪比情况下,达到相同误码率时,与传统MCMC算法相比,改进MCMC算法的复杂度明显降低。     

Multiscale Bayesian texture segmentation using neural networks and Markov random fields

This paper presents a wavelet-based texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields (MRF) in a multi-scale Bayesian framework. Inputs and outputs of MLP networks are constructed to estimate a posterior probability. The multi-scale features produced by multi-level wavelet decompositions of textured images are classified at each scale by maximum a posterior (MAP) classification and the posterior probabilities from MLP networks. An MRF model is used in order to model the prior distribution of each texture class, and a factor, which fuses the classification information through scales and acts as a guide for the labeling decision, is incorporated into the MAP classification of each scale. By fusing the multi-scale MAP classifications sequentially from coarse to fine scales, our proposed method gets the final and improved segmentation result at the finest scale. In this fusion process, the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. Our texture segmentation method was applied to segmentation of gray-level textured images. The proposed segmentation method shows better performance than texture segmentation using the hidden Markov trees (HMT) model and the HMTseg algorithm, which is a multi-scale Bayesian image segmentation algorithm.     

Using capture-recapture data and hybrid Monte Carlo sampling to estimate an animal population affected by an environmental catastrophe

We propose a dynamic model for the evolution of an open animal population that is subject to an environmental catastrophe. The model incorporates a capture-recapture experiment often conducted for studying wildlife population, and enables inferences on the population size and possible effect of the catastrophe. A Bayesian approach is used to model unobserved quantities in the problem as latent variables and Markov chain Monte Carlo (MCMC) is used for posterior computation. Because the particular interrelationship between observed and latent variables negates the feasibility of standard MCMC methods, we propose a hybrid Monte Carlo approach that integrates a Gibbs sampler with the strategies of sequential importance sampling (SIS) and acceptance-rejection (AR) sampling for model estimation. We develop results on how to construct effective proposal densities for the SIS scheme. The approach is illustrated through a simulation study, and is applied to data from a mountain pygmy possum (Burramys Parvus) population that was affected by a bushfire.     

Convergence Diagnostics for Gibbs Sampler via Maximum Likelihood Estimation

A diagnostic procedure based on maximum likelihood estimation, to study the convergence of the Markov chain produced by Gibbs sampler, is presented. The unbiasedness, consistent and asymptotic normality are considered for the estimation of the parameters produced by the procedure. An example is provided to illustrate the procedure, and the numerical result is consistent with the theoretical one.     

水利水电工程地质勘察中取土器及取样方法的探讨

为了提高工程地质勘察中提取原状土样的质量 ,针对不同的地层情况选择合理技术参数的取土器非常重要 ,本文介绍了几种取土器 ,并对所研制的取土器的管靴刃口形式的局部改进和取土方法进行了说明     

取样器测湿修正新方法

空调系统通过取样器测湿时，对结果必须修正．原有修正方法只适用于干湿球湿度计，使用不便．本文提出一种新修正方法，适用于各种类型湿度传感器．     

气体-颗粒两相流动力学及其在气溶胶采样中的应用

大气颗粒物切割器的工作原理主要是重力沉降、离心分离和冲击分离,是气体-颗粒物两相流的动力学分离过程.气固两相流动力学方程可以较方便地用来分析大气颗粒物在切割器中的运动轨迹,具体计算过程中阻力系数CD和边界条件应根据具体情况分析选定.本文给出了两个利用数值计算模拟切割器工作的例子,对采样仪的采样效率、切割粒径及切割器结构尺寸等设计参数对切割粒径的影响进行计算,表明两相流动力学是对采样切割器中颗粒物的运动进行数值模拟的有效工具,能对采样仪、切割器的设计、制造提供有力的帮助.     

频率选择性衰落中基于MCMC的调制分类

为解决在频率选择性衰落信道中,频偏、相偏和噪声功率等多参数未知的幅相调制信号的调制分类问题,提出一种新颖的基于马尔可夫链蒙特卡罗(MCMC)方法的调制分类算法．给出最大后验概率分类器框架,利用MCMC方法产生未知参数和发送符号的各态历经随机样本,用蒙特卡罗积分近似估计分类器框架中无法得到封闭表达式的后验概率,MCMC方法所用到的未知参数和发送符号的后验条件概率密度函数(pdf)由接收信号先验pdf推导得出．数值仿真证明了该算法的收敛性及分类器良好的分类性能．